Now, for addressing self-balancing, there are several alternatives, being two of the most popular the Adelson-Velsky and Landis (AVL) tree and the Red-Black Tree. In the second, formerly known as symmetric binary B-tree , each node has an extra bit which is often interpreted as the color red or black of the node, so that explains its name.
More specifically, we used a Red-Black Tree scheme to achieve self-balancing of the binary search tree and performed the SBOST operations according to the algorithms described in the previous section.
The SBOST was implemented from a Red-Black Tree, but other alternatives for Self-Balanced Binary Search Trees could be adopted as well.
Beside this, Red-Black tree is used to store the address of each object in a particular cell, which further helps to minimize the object search time.
In the proposed method, after launching a ray, firstly, it performs the intersection test between the ray and two surfaces (if the ray travels into first quadrant, then it will choose either left or bottom surface; for the second quadrant, it will choose either right or bottom surface; for the third quadrant, it will choose either top or right surface; and for the fourth quadrant, it will choose the top or left surface) of each object in a particular cell (stored in the Red-Black tree).
(4) Retrieve the Red-Black tree from a particular cell of the Cell list (as presented in Section 3.2), from where the ray segment started.
(5) Retrieve each object from the object list by using the object addresses stored in each node of the Red-Black tree.
The text is to introduce the concept of a red-black tree and to teach the building process of a red-black tree.
The pretest includes a Binary Tree Definition Test (BTDT) and a Red-black Tree Definition Test (RTDT).
The content-related prompts involved some domain-related or content-related information, such as "Is this a red-black tree? Why?" and "Why is node 2 located at the right child-node of node 1?".
New features enhancing this revised sixth edition include range-based for loops and threads; Trees Plus, that emphasizes balancing of search trees by covering AVL Trees, Red-Black Trees
, and B-Trees; a new chapter on Sets, Maps, and Hashing; Sorting, which now includes practical performance issues and parallel merge sort; and new chapters in the second half of the text that are now easier to assign in alternate orders, supporting a wider range of course goals and organizations.
Further chapters introduce more sophisticated computer science topics such as recursion, sorting, searching, linked lists, stacks, hashes, and binary and red-black trees
. All of these topics are covered with considerable rigor and in the context of the Java language.